1. Field of the Invention
Embodiments of the present invention generally relate to the field of information technology, and more specifically, to a method, apparatus, and system for selecting a solution for carbon emission prediction.
2. Related Art
As the energy and environmental issue gets increasingly tough, carbon emission has received more and more attention. Carbon emission is a general term or short for greenhouse gas emission. Since excessive carbon emissions will exert an adverse or even irreversible impact on the environment, the control of carbon emissions is one of important issues confronting modern production, manufacture, logistics, and other aspects.
After a demand with respect to a specific production or transport project is determined and before the project is put into actual operation, in addition to predicting and evaluating the key performance index (KPI), it is expected to predict carbon emissions as precisely as possible so as to adjust the plan or scheme of the protect according to the prediction and further reduce carbon emissions for satisfying rules and regulations, international standards, etc. In the context of the disclosure, term “demand” refers to any carbon emissions-related requirement, provision, standard or other aspect in production, manufacture, transport and other projects, including but not limited to amount of coal used, amount of power used, amount of fuel used, amount of natural gas used, storage area (e.g., warehouse area), amount of machine used, moving distance of machine, heating time, lighting time, and so on.
There exist several solutions for predicting carbon emission, including but not limited to IPCC (the Intergovernmental Panel on Climate Change) Guideline, GHG (the Greenhouse Effect) Protocol Initiative, PAS2050, and so on. Generally speaking, existing solutions for carbon emission prediction can be divided into two types, namely data-based prediction solutions (referred to as “data prediction solutions” for short) and non-data-based prediction solutions (referred to as “rule prediction solutions” for short). In a data prediction solution, a demand is quantized, and various value operations are performed on quantized values to obtain a result of carbon emission prediction. By contrast, in a non-data-based prediction solution a result of carbon emissions prediction is obtained based on a series of rules and logical judgment. It should be noted that the “non-data-based prediction solution” and “rule prediction solution” can be used interchangeably in the context of the disclosure.
After a project is put into practice, actual measurements of carbon emission can be measured by various means. Subsequently, a prediction precision can be obtained by comparing the actual measurements of carbon emission with the prediction result. As known in the art, data prediction and rule prediction can have different precisions for different demands. It is to be understood that for some demands, a data prediction solution obtains a more approximate prediction result to actual measurements than a rule prediction solution, while for other demands, a rule prediction solution obtains a more approximate prediction result to actual measurements than data prediction. In other words, precisions of these two types of solutions for carbon emission prediction are related to demands.